Deep-learning end-to-end autoencoder for the joint mitigation of chromatic dispersion andKerr nonlinearity in optical communication systems

用于联合减轻光通信系统中色散和克尔非线性的深度学习端到端自动编码器

• 批准号: 460943258
• 负责人: Professor Dr.-Ing. Stephan ten Brink
• 金额: --
• 依托单位: Institut für Nachrichtenübertragung (INÜ)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/460943258?language=en
• 关键词: Deep; learning; end; autoencoder; joint

In this project, we seek to improve the spectral efficiency of optical communication systems by using methods from artificial intelligence, and, as a result, increase reach and data rates. We put emphasis on the interpretability of the learnings -- facilitated by using the idea of "architectural templates" -- to enable the derivation of insights about the optical channel and its capacity. At first glance, optical fibers promise a seemingly infinite bandwidth, a static propagation environment combined with low noise and small attenuation coefficients. However, in the previous decades, the exponential growth of data-rates and the fast progress in circuit design have pushed the occupied bandwidth and sampling rates towards an operation point where even the seemingly perfect optical fiber is dominated by nonlinearity that cannot be neglected nor compensated easily anymore. From an engineering perspective, this opens up an exciting field of research to mitigate such impairments. At the same time, deep learning-driven communications has become a promising and active research topic, in particular in the wireless domain. It has been shown that end-to-end learning of transmitter and receiver in a joint manner via an "autoencoder" allows to find new signal constellations and even waveforms for (almost) arbitrary channels that are not restricted to linear scenarios, and that have not been accessible via classic methods before. We, thus, are attracted by the challenges of signaling across the nonlinear optical fiber and the conceptual simplicity of the end-to-end learning framework. Based on our previous results in wireless and optical communications using neural networks, we seek to come up with novel architectural templates and learning concepts tailored to the optical fiber channel, for increasing spectral efficiency, reach and data rates over single wavelength channels, as well as over wavelength division multiplex-based systems. Also, we intend to study the relationship of the novel architectural templates (with learned signal constellations and waveforms) to Eigenvalue-based optical communications using the nonlinear Fourier transformation, to find further ideas for jointly compensating chromatic dispersion and fiber nonlinearities.

在这个项目中，我们试图通过使用人工智能的方法来提高光通信系统的频谱效率，从而提高覆盖范围和数据速率。我们强调学习的可解释性-通过使用“架构模板”的想法来促进-以获得有关光通道及其容量的见解。乍看之下，光纤承诺一个看似无限的带宽，一个静态的传播环境结合低噪声和小衰减系数。然而，在过去的几十年里，数据速率的指数增长和电路设计的快速进步已经将占用的带宽和采样速率推向了一个工作点，在这个工作点上，即使是看似完美的光纤也受到非线性的影响，这种非线性不能被忽视，也不能轻易补偿。从工程的角度来看，这开辟了一个令人兴奋的研究领域，以减轻这种损害。与此同时，深度学习驱动的通信已经成为一个有前途和活跃的研究课题，特别是在无线领域。已经表明，通过“自动编码器”以联合方式对发射机和接收机进行端到端学习，可以为（几乎）任意信道找到新的信号星座甚至波形，这些信道不限于线性场景，并且以前无法通过经典方法访问。因此，我们被非线性光纤中的信号传输挑战和端到端学习框架的概念简单性所吸引。基于我们以前在使用神经网络的无线和光通信中的结果，我们试图提出针对光纤信道的新架构模板和学习概念，以提高单波长信道以及波分复用系统的频谱效率，覆盖范围和数据速率。此外，我们打算研究新的架构模板（与学习的信号星座和波形）的关系，以基于本征值的光通信使用的非线性傅里叶变换，找到进一步的想法，共同补偿色散和光纤非线性。